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...ype to replace the placeholders with the actual type. However, pointers to the opaque type aren't being replaced with pointers to the actual type, which causes errors further down the line. For instance, if I had a type: %t = { %t*, ... } My pass would begin constructing a new type, say %t_1, and would replace all instances of %t with an opaque type, so... $t_0 = opaque %t_1 = { %t_0*, ... } At the end, it calls t_1->refineAbstracType(t_0, t_1) to replace instances of t_0 with t_1. But the resulting type I get is still %t_1 = { %t_0*, ... } Is this a bug, or is it supposed to...

...s " opt -std-compile-opts test.ll -S -o -" . But the output shows that there is code motion around the barrier intrinsics. test.ll ------- ; ModuleID = 'test.bc' define void @test(i16* %I_0, i16* %I_1, i16* %I_2, i16* %I_3, i16* %O_0) { entry: %T_0 = load volatile i16* %I_0 %T_1 = load volatile i16* %I_1 %T_2 = load volatile i16* %I_2 %T_3 = load volatile i16* %I_3 call void @llvm.nvvm.barrier0() %T_5 = add i16 %T_1, %T_3 call void @llvm.nvvm.barrier0() %T_7 = mul i16 %T_0, %T_2 %T_8 = xor i16 %T_2, %T_0 %T_9 = mul i16 %T_0, %T_1 call void @llvm.nvvm.barr...

is there any guarantee that the nvptx intrinsic "llvm.nvvm.barrier0" will not be moved around by opt ? In other words, can I expect all the instructions above "llvm.nvvm.barrier0" to remain above it and those below it to remain below, after all the opt passes are run ? If that is not the case, is there a way to define such an intrinsic ? Thanks. -------------- next part

I'm trying to use the rsync algorithm for incremental backups. After a quick look at rsync I saw the batch mode operations, and I thought that maybe I can modify them for incremental backups. What is needed is to add an option to save the checksums of all the files of the level 0 backup and a second option to use the level n checksum to calculate the delta batch files for the level n+1

...get it to produce NAs. Any suggestions? Yours, Simon Kiss mydata<-list(x=seq(as.Date("2007-05-01"), as.Date("2009-09-10"),"days"), y=seq(as.Date("2007-06-16"), as.Date("2009-11-12"),"days")) table(format(mydata[[1]], "%Y")) t_1<-hist(as.numeric(mydata[[1]], breaks=c("14056", "14421")))$counts cut(mydata[[1]], breaks=c(as.Date("2008-06-26"), ("2009=06-26"))) ********************************* Simon J. Kiss, PhD Assistant Professor, Wilfrid Laurier University 73 George Street Bran...

Hello, can anybody here explain what a Jonckheere-Terpstra test is and whether it is implemented in R? I just know it's a non-parametric test, otherwise I've no clue about it ;-( . Are there alternatives to this test? thanks for help, Arne

...  y ~ x_1[, 1] + x_2[, 1] + x_1[, 2] + x_2[, 2]   Where   x_1[, i] = cos(2 * pi * t / T_i) x_2[, i] = sin(2 * pi * t / T_i)   i = 1, 2   Data have two columns: t and y.   As you can see, I have a multiple components model, with rithm and without trends, and I have a fundamental period (T_1 = 24 hour; T_2 = 12 hour).   I have to compare the parameters between the two models (one for each subject), using a parametric test as described in the doc I adjunt (page 500, Parametric solution):   I have to reach results as follow:   ______________________________________________________...

...o estimate the model in R? using garchFit or garchOxFit? For example, Observations is {x,y}_t,there may be some relation between x and y. the model is y_t=gamma0 + *gamma1*x_t*+psi*e_{t-1}+e_t the gamma1*x_t is regression. e_t=sqrt(h_t)*N(0,1) h_t=alpha0+alpha1*e_t^2+beta*h_{t_1}~~~~~~~GARCH(1,1). I didn't know how to estimate the model using function garchFit or garchOxFit or other functions? because the argument in garchFit/garchOxFit is formular.mean=~arma(1,1). Do you have some instrucitons? thank you very much for you help. Best wishes Ma Yuchao...

...is usually 3 to 5, whereas N is of the order of 1000). Every object has its own label L_i, i=1...N, that is known. For each of these objects I measure some property in time (let's say I measure it Q times in a given time interval), i.e. the i-th object has an associated file {t, y}, where t=(t_1,t_2....t_Q) and y=(y_1,y_2,...y_Q). My problem is then to come up with an algorithm that after learning on the training dataset, can guess the labels of a testing dataset. The difference with respect to the datamining I have done so far is that I do not have a set of properties for every object (...

...lues to attr_accessor dynamically in controller. And am sending the data object to display in Yaml format. I am getting the response along with changed_attributes but I don''t want changed_attributes details in my response. build Model code class Test < ActiveRecord::Base attr_accessor: t_1, t_2, t_3, t_4 end In Test Controller code: #build is controller object tb = ["[5, 30.14]","[0, 12.0]","[1, 11.0]","[2, 11.0]"] $i=1 while $i <= tb.length do $tbVal = "t_#{$i}" build["#{$tbVal}"] = tb[$i-1] $i += 1 wants.yaml { rende...

...actually programmed a bootstrap using some homebrew code and it worked fine. But, I am trying to use the more efficient boot function. I have placed some sample data for replication of my problem at the bottom of this email. For the sample problem, I have 10 subjects each with 5 observations Y_t = (t_1, ..., t_5). Consider these 'longitudinal' data. So, I use reshape to put into the long format in order to regress the observations onto time, a time-varying covariate. I do the regression for each individual separately: Y_{t} = \mu + \beta(time) + \epsilon_{t} To get the statistic of inte...

...package, which is not yet supposed to work, it seems that glm does not work either. Without Survival glm seems to work. I am not sure whether this gives a clue concerning the problems with Survival. One interesting application is to Weibull data which is only observed at single inspection points T_1, ..., T_n, not all the same and contained in insp.times. The only thing that is known about the actual lifetimes X_1, ..., X_n is whether X_i < T_i or not, giving me the 0,1 vector status. One can then get the mle's of the Weibull scale (alpha) and shape (beta) parameters by using dat <...